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Fulltext Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

Albadr MAA, Tiun S, Ayob M, Al-Dhief FT, Omar K, Hamzah FA

PLoS One, 2020;15(12):e0242899.
PMID: 33320858 DOI: 10.1371/journal.pone.0242899

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

Matched MeSH terms: Thorax/physiopathology
Psychometric evaluation of the shortened version of the Functional Difficulties Questionnaire to assess thoracic physical function

Katijjahbe MA, Denehy L, Granger CL, Royse A, Royse C, Logie S, et al.

Clin Rehabil, 2020 Jan;34(1):132-140.
PMID: 31610700 DOI: 10.1177/0269215519879476

OBJECTIVE: The aim of this study was to investigate the psychometric properties of the shortened version of the Functional Difficulties Questionnaire (FDQ).
DESIGN: This is a multisite observational study.
SETTING: The study was conducted in four tertiary care hospitals in Australia.
SUBJECTS: A total of 225 participants, following cardiac surgery, were involved in the study.
INTERVENTION: Participants completed the original 13-item FDQ and other measures of physical function, pain and health-related quality of life.
METHOD: Item reduction was utilized to develop the shortened version. Reliability was evaluated using intraclass correlation coefficients (ICCs), the smallest detectable change and Bland-Altman plots. The validity and responsiveness were evaluated using correlation. Anchor and distribution-based calculation was used to calculate the minimal clinical important difference (MCID).
RESULTS: Item reduction resulted in the creation of a 10-item shortened version of the questionnaire (FDQ-s). Within the cohort of cardiac surgery patient, the mean (SD) for the FDQ-s was 38.7 (19.61) at baseline; 15.5 (14.01) at four weeks and 7.9 (12.01) at three months. Validity: excellent internal consistency (Cronbach's α > 0.90) and fair-to-excellent construct validity (>0.4). Reliability: internal consistency was excellent (Cronbach's α > 0.8). The FDQ-s had excellent test-retest reliability (ICC = 0.89-0.92). Strong responsiveness overtime was demonstrated with large effect sizes (Cohen's d > 1.0). The MCID of the FDQ-s was calculated between 4 and 10 out of 100 (in cm).
CONCLUSION: The FDQ-s demonstrated robust psychometric properties as a measurement tool of physical function of the thoracic region following cardiac surgery.

Matched MeSH terms: Thorax/physiopathology*

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